Latest from the Amazon Cloud-
hi1.4xlarge instances come with eight virtual cores that can deliver 35 EC2 Compute Units (ECUs) of CPU performance, 60.5 GiB of RAM, and 2 TiB of storage capacity across two SSD-based storage volumes. Customers using hi1.4xlarge instances for their applications can expect over 120,000 4 KB random write IOPS, and as many as 85,000 random write IOPS (depending on active LBA span). These instances are available on a 10 Gbps network, with the ability to launch instances into cluster placement groups for low-latency, full-bisection bandwidth networking.
High I/O instances are currently available in three Availability Zones in US East (N. Virginia) and two Availability Zones in EU West (Ireland) regions. Other regions will be supported in the coming months. You can launch hi1.4xlarge instances as On Demand instances starting at $3.10/hour, and purchase them as Reserved Instances
High I/O Instances
Instances of this family provide very high instance storage I/O performance and are ideally suited for many high performance database workloads. Example applications include NoSQL databases like Cassandra and MongoDB. High I/O instances are backed by Solid State Drives (SSD), and also provide high levels of CPU, memory and network performance.
High I/O Quadruple Extra Large Instance
60.5 GB of memory
35 EC2 Compute Units (8 virtual cores with 4.4 EC2 Compute Units each)
2 SSD-based volumes each with 1024 GB of instance storage
I/O Performance: Very High (10 Gigabit Ethernet)
Storage I/O Performance: Very High*
API name: hi1.4xlarge
*Using Linux paravirtual (PV) AMIs, High I/O Quadruple Extra Large instances can deliver more than 120,000 4 KB random read IOPS and between 10,000 and 85,000 4 KB random write IOPS (depending on active logical block addressing span) to applications. For hardware virtual machines (HVM) and Windows AMIs, performance is approximately 90,000 4 KB random read IOPS and between 9,000 and 75,000 4 KB random write IOPS. The maximum sequential throughput on all AMI types (Linux PV, Linux HVM, and Windows) per second is approximately 2 GB read and 1.1 GB write.
Western countries are running out of people to fight their wars. This is even more acute given the traditional and current demographic trends in both armed forces and general populations.
A shift to cyber conflict can help the West maintain parity over Eastern methods of assymetrical warfare (by human attrition /cyber conflict).
Declining resources will lead to converging conflicts of interest and dynamics in balance of power in the 21 st century.
The launch of Sputnik by USSR led to the moon shot rush by the US.1960s
The proposed announcement of StarWars by USA led to unsustainable defence expenditure by USSR.1980s
The threat of cyber conflict and espionage by China (and Russian cyber actions in war with Georgia) has led to increasing budgets for cyber conflict research and defense in USA. -2010s
If we do not learn from history, we are condemned to repeat it.
Declining Populations in the West and Rising Populations in the East in the 21 st century. The difference in military age personnel would be even more severe, due to more rapid aging in the west.
Economic output will be proportional to number of people employed as economies reach similar stages of maturity (Factor-Manufacturing-Services-Innovation)
GDP projections to 2050:
Western defence forces would not be able to afford a human attrition intensive war by 2030 given current demographic trends (both growth and aging). Existing balance of power could be maintained if resources are either shared or warfare is moved to cyber space. Technological advances can help augment resources reducing case for conflict scenarios.
Will the Internet be used by US against China in the 21 st century as Opium was used by GB in the 19th? Time will tell :)
Message from the guys at Palo Alto— Why dont they just make videos using Sal Academy’s help?
We’re sorry to have to tell you that our Machine Learning course will be delayed further. There have naturally been legal and administrative issues to be sorted out in offering Stanford classes freely to the outside world, and it’s just been taking time. We have, however, been able to take advantage of the extra time to debug and improve our course content!
We now expect that the course will start either late in February or early in March. We will let you know as soon as we hear a definite date. We apologize for the lack of communication in recent weeks; we kept hoping we would have a concrete launch date to give you, but that date has kept slipping.
Thanks so much for your patience! We are really sorry for repeatedly making you wait, and for any interference this causes in your schedules. We’re as excited and anxious as you are to get started, and we both look forward to your joining us soon in Machine Learning!
Andrew Ng and the ML Course Staff